Home » date » 2010 » Dec » 13 »

Paper Celebrity MR

*The author of this computation has been verified*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Mon, 13 Dec 2010 19:43:25 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/13/t1292270254hirx8j083vo4k4i.htm/, Retrieved Mon, 13 Dec 2010 20:57:37 +0100

BibTeX entries for LaTeX users:

@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/13/t1292270254hirx8j083vo4k4i.htm/},
    year = {2010},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2010},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

6 2 1 3 11 16 14 5 4 1 1 1 11 13 11 12 5 1 1 3 15 16 11 11 4 1 1 3 11 6 9 6 4 1 2 3 9 11 11 12 6 1 1 3 14 13 16 11 6 1 1 1 12 15 13 12 4 2 4 3 6 9 11 7 4 1 1 3 4 6 4 8 6 1 1 1 13 11 15 13 4 1 1 1 12 9 13 12 6 1 1 3 10 4 13 13 5 1 1 1 12 8 13 12 4 1 3 3 9 11 11 12 6 2 1 3 16 16 15 11 3 2 1 1 13 5 12 12 5 1 1 1 12 6 14 12 6 1 6 1 11 7 13 12 4 2 1 3 12 16 13 11 6 2 1 1 12 12 12 13 2 1 1 3 11 7 13 9 7 2 1 3 16 13 14 11 5 1 1 1 9 12 13 11 2 2 1 3 8 10 15 11 4 1 1 1 11 12 12 9 4 2 1 4 9 8 10 11 6 2 1 3 16 15 14 12 6 1 1 3 14 15 13 12 5 2 1 3 10 10 11 10 6 1 4 3 14 13 15 12 6 2 1 1 16 16 14 12 4 1 1 3 12 10 13 12 6 2 1 3 13 14 14 9 6 1 1 3 16 16 16 9 6 1 1 3 15 13 13 12 2 2 1 1 5 4 5 14 4 2 1 3 12 7 11 12 5 1 1 1 11 15 10 11 3 1 1 2 15 5 11 9 7 2 1 3 15 14 15 11 5 1 1 1 12 11 15 7 3 1 1 1 5 8 12 15 8 1 1 3 16 14 15 11 8 1 1 3 16 12 15 12 5 2 2 1 12 12 14 12 6 2 1 3 6 15 11 9 3 2 1 3 7 8 12 12 5 2 1 3 14 16 12 11 4 2 2 3 8 9 12 11 5 1 4 3 12 13 13 8 5 2 1 1 10 8 9 7 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Multiple Linear Regression - Estimated Regression Equation

Celebrity[t] = + 0.500464733012453 -0.14217988308427Gender[t] + 0.0812069443486665Raised[t] + 0.092011267173835Marital[t] + 0.16911978017478Popularity[t] + 0.0953250302116789KnowingPeople[t] + 0.129653715719074Liked[t] -0.0261759550770048FindingFriends[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STAT H0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	0.500464733012453	0.812915	0.6156	0.539145	0.269572
Gender	-0.14217988308427	0.18642	-0.7627	0.446951	0.223476
Raised	0.0812069443486665	0.102256	0.7941	0.428472	0.214236
Marital	0.092011267173835	0.089266	1.0308	0.304457	0.152228
Popularity	0.16911978017478	0.041238	4.1011	7e-05	3.5e-05
KnowingPeople	0.0953250302116789	0.032005	2.9784	0.003424	0.001712
Liked	0.129653715719074	0.050154	2.5851	0.010772	0.005386
FindingFriends	-0.0261759550770048	0.049327	-0.5307	0.596506	0.298253

Multiple Linear Regression - Regression Statistics
Multiple R	0.680555914549806
R-squared	0.463156352828723
Adjusted R-squared	0.435925153334528
F-TEST (value)	17.0082978874086
F-TEST (DF numerator)	7
F-TEST (DF denominator)	138
p-value	4.44089209850063e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.04735303859423
Sum Squared Residuals	151.378877468454

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	6	5.64313602270554	0.356863977294461
2	4	4.74312544811085	-0.743125448110848
3	5	5.91577814886968	-0.915778148869679
4	4	4.15762107000065	-0.157621070000646
5	4	4.47946530603427	-0.479465306034267
6	6	6.10895185665523	-0.108951856655235
7	6	5.36220272014714	0.637797279852865
8	4	3.93256968608466	0.0674303139153416
9	4	2.27316212002781	1.72683787997219
10	6	5.38315385583634	0.616846144163656
11	4	4.79025253887706	-0.790252538877062
12	6	4.13323440673977	1.86676559326023
13	5	4.69492750866538	0.305072491334617
14	4	4.56067225038293	-0.560672250382935
15	6	6.46133290883649	-0.461332908836487
16	3	4.30623859940178	-1.30623859940178
17	5	4.6339311639611	0.3660688360389
18	6	4.83651742002226	1.16348257997774
19	4	5.52554635669922	-1.52554635669922
20	6	4.77821807563175	1.22178192436825
21	2	4.69303309785761	-2.69303309785761
22	7	6.04570410248238	0.954295897517624
23	5	4.59504424406476	0.404955755935236
24	2	4.53642448616818	-2.53642448616818
25	4	4.85598199884926	-0.855981998849259
26	4	3.95863689449806	0.0413631055019396
27	6	6.21017820782873	-0.210178207828729
28	6	5.88446481484436	0.115535185155635
29	5	4.38222513871844	0.617774861281558
30	6	6.19674301890516	-0.196743018905156
31	6	6.12148070369274	-0.121480703692738
32	4	5.06960010343641	-1.06960010343641
33	6	5.68602170232372	0.313978297676275
34	6	6.78551841779384	-0.785518417793841
35	6	5.86293453459579	0.137065465404213
36	2	1.89802740760433	0.101972592395666
37	4	4.38213769827896	-0.382137698278955
38	5	4.83029774789214	0.169702252107863
39	3	4.82754345952139	-1.82754345952139
40	7	6.10156306823835	0.89843693176165
41	5	5.37108980612359	-0.371089806123593
42	3	3.30290746649184	-0.302907466491836
43	8	6.4128627314974	1.5871372685026
44	8	6.19603671599704	1.80396328400296
45	5	5.14490840649557	-0.14490840649557
46	6	4.20854712415472	1.79145287584528
47	3	3.76151754333581	-0.76151754333581
48	5	5.7341322013297	-0.734132201329704
49	4	4.13334525314794	-0.13334525314794
50	5	5.70389984742547	-0.703899847425466
51	5	3.82677297774028	1.17322702225972
52	6	5.34889270856268	0.651107291437319
53	5	5.76236627331116	-0.762366273311165
54	6	6.02490002734767	-0.024900027347671
55	6	4.68622753475429	1.31377246524571
56	4	3.47437820385203	0.525621796147967
57	8	6.51634049213947	1.48365950786053
58	6	4.85379593691222	1.14620406308778
59	4	4.06029103922966	-0.060291039229664
60	6	5.6879161131315	0.3120838868685
61	5	5.50361181277025	-0.503611812770245
62	5	5.63290585015621	-0.63290585015621
63	6	6.52885166062481	-0.528851660624815
64	6	6.20126153538483	-0.201261535384833
65	6	5.55163487133674	0.448365128663263
66	6	4.41997343938831	1.58002656061169
67	6	5.63124862001473	0.368751379985267
68	6	5.79378545946111	0.206214540538887
69	7	5.36107225135287	1.63892774864713
70	4	4.09046575402852	-0.0904657540285158
71	4	4.09738051687767	-0.0973805168776744
72	3	5.26764163194896	-2.26764163194896
73	6	6.51719187459246	-0.517191874592461
74	5	5.98443551988447	-0.98443551988447
75	5	5.34067243989856	-0.340672439898557
76	3	3.63103585113242	-0.631035851132423
77	5	5.50979222007334	-0.509792220073337
78	4	4.67235420006202	-0.672354200062023
79	3	4.51179141399803	-1.51179141399803
80	7	6.73316650763983	0.266833492360169
81	4	3.39571699977351	0.60428300022649
82	4	5.5805539400022	-1.5805539400022
83	5	5.45870715422913	-0.458707154229126
84	6	5.1500223794752	0.8499776205248
85	2	3.41427387010287	-1.41427387010287
86	2	2.48188002633295	-0.481880026332946
87	6	4.86615163775424	1.13384836224576
88	4	3.48216997877646	0.517830021223539
89	5	4.91926748992175	0.0807325100782496
90	6	5.31260207401378	0.687397925986222
91	7	6.52296801821516	0.477031981784844
92	8	6.15504544718663	1.84495455281337
93	6	6.44325204508449	-0.443252045084487
94	6	5.23689626770073	0.763103732299275
95	3	4.52589516893009	-1.52589516893009
96	7	6.64446900350384	0.35553099649616
97	3	4.38419581518341	-1.38419581518341
98	6	5.80610333538234	0.193896664617661
99	4	4.20635533480116	-0.206355334801163
100	4	4.83766036264688	-0.837660362646882
101	6	4.6063525021962	1.3936474978038
102	6	6.36441596012699	-0.364415960126991
103	6	3.86003930967841	2.13996069032159
104	4	5.68508287943125	-1.68508287943125
105	7	5.46690654045515	1.53309345954485
106	5	5.27313600955807	-0.273136009558073
107	7	6.39000053945824	0.609999460541762
108	4	4.36343797205832	-0.363437972058323
109	6	5.58463164505355	0.41536835494645
110	6	6.21463908520302	-0.21463908520302
111	6	5.11879648588937	0.881203514110628
112	5	5.11104801913938	-0.111048019139377
113	5	5.13483785928872	-0.134837859288725
114	6	5.54622525449481	0.453774745505195
115	7	5.45554589911155	1.54445410088845
116	4	5.16880954613532	-1.16880954613532
117	4	5.26025016385977	-1.26025016385977
118	8	5.77062485586619	2.22937514413381
119	6	5.46217342518724	0.537826574812759
120	3	4.39110407426271	-1.39110407426271
121	4	4.69577398779247	-0.69577398779247
122	5	4.5653612333858	0.434638766614205
123	5	4.18994475494874	0.810055245051262
124	6	5.85040836723059	0.149591632769408
125	8	6.7710022487492	1.22899775125081
126	2	5.21793921444517	-3.21793921444517
127	4	3.96344080466328	0.0365591953367177
128	7	6.23777759589	0.762222404110002
129	5	4.1298147915773	0.870185208422704
130	6	5.54348214090635	0.456517859093654
131	6	6.25703306070132	-0.25703306070132
132	4	5.34684331908169	-1.34684331908169
133	5	5.52545891625973	-0.525458916259732
134	6	5.72121676916396	0.278783230836039
135	6	5.8229767660202	0.177023233979801
136	6	5.67595791554338	0.324042084456618
137	6	5.85111467013871	0.148885329861289
138	5	5.47707617936013	-0.477076179360131
139	5	5.00573897156748	-0.00573897156747607
140	6	6.80534861280874	-0.805348612808743
141	4	4.59597189217495	-0.595971892174951
142	6	4.25473539202846	1.74526460797154
143	3	4.1963000431308	-1.1963000431308
144	6	4.54847662796564	1.45152337203436
145	8	6.51634049213947	1.48365950786053
146	4	6.34722071196469	-2.34722071196469

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.477026702854833	0.954053405709667	0.522973297145167
12	0.407866691851486	0.815733383702972	0.592133308148514
13	0.269925232372258	0.539850464744516	0.730074767627742
14	0.166654662633864	0.333309325267727	0.833345337366136
15	0.0991006519856311	0.198201303971262	0.900899348014369
16	0.104899017956924	0.209798035913847	0.895100982043076
17	0.0622434876158858	0.124486975231772	0.937756512384114
18	0.2858984247357	0.571796849471399	0.7141015752643
19	0.248105045849833	0.496210091699666	0.751894954150167
20	0.454923422330365	0.90984684466073	0.545076577669635
21	0.835615755273061	0.328768489453878	0.164384244726939
22	0.881822420403215	0.23635515919357	0.118177579596785
23	0.84142126705237	0.317157465895259	0.158578732947629
24	0.932640610100932	0.134718779798136	0.0673593898990678
25	0.923535137289181	0.152929725421637	0.0764648627108186
26	0.896729370468012	0.206541259063977	0.103270629531988
27	0.863602779194152	0.272794441611695	0.136397220805848
28	0.826461849864964	0.347076300270072	0.173538150135036
29	0.801526595940365	0.396946808119271	0.198473404059635
30	0.752988307317186	0.494023385365628	0.247011692682814
31	0.699435082907645	0.601129834184711	0.300564917092355
32	0.676960101776723	0.646079796446553	0.323039898223276
33	0.646989331156861	0.706021337686277	0.353010668843139
34	0.597312225929027	0.805375548141946	0.402687774070973
35	0.541468256368549	0.917063487262902	0.458531743631451
36	0.506701103497714	0.986597793004572	0.493298896502286
37	0.453023421635717	0.906046843271434	0.546976578364283
38	0.39539754621691	0.79079509243382	0.60460245378309
39	0.494393126830779	0.988786253661558	0.505606873169221
40	0.508023433798123	0.983953132403754	0.491976566201877
41	0.455310859606834	0.910621719213667	0.544689140393166
42	0.404009267388703	0.808018534777406	0.595990732611297
43	0.510637008913054	0.978725982173891	0.489362991086946
44	0.62321751676065	0.7535649664787	0.37678248323935
45	0.570159440420598	0.859681119158805	0.429840559579402
46	0.667530813789254	0.664938372421492	0.332469186210746
47	0.6372952186807	0.7254095626386	0.3627047813193
48	0.616403447322411	0.767193105355178	0.383596552677589
49	0.564516149210734	0.870967701578532	0.435483850789266
50	0.537414085446596	0.925171829106808	0.462585914553404
51	0.550296189614432	0.899407620771136	0.449703810385568
52	0.514031530280932	0.971936939438137	0.485968469719068
53	0.482138176450282	0.964276352900564	0.517861823549718
54	0.433116600851798	0.866233201703597	0.566883399148202
55	0.461583203804797	0.923166407609593	0.538416796195203
56	0.427591102986589	0.855182205973178	0.572408897013411
57	0.483116147234488	0.966232294468977	0.516883852765512
58	0.498658999906889	0.997317999813779	0.501341000093111
59	0.447661085310141	0.895322170620282	0.552338914689859
60	0.400952302507729	0.801904605015458	0.599047697492271
61	0.363632731298662	0.727265462597324	0.636367268701338
62	0.331840475726608	0.663680951453215	0.668159524273392
63	0.298191480259835	0.59638296051967	0.701808519740165
64	0.257704236196919	0.515408472393839	0.742295763803081
65	0.224346930465845	0.44869386093169	0.775653069534155
66	0.278029495297517	0.556058990595034	0.721970504702483
67	0.247763729576288	0.495527459152575	0.752236270423712
68	0.211583818157161	0.423167636314322	0.788416181842839
69	0.260398019426745	0.520796038853491	0.739601980573255
70	0.225878563598547	0.451757127197094	0.774121436401453
71	0.191037831059905	0.382075662119809	0.808962168940095
72	0.333783424138905	0.66756684827781	0.666216575861095
73	0.303299143699324	0.606598287398649	0.696700856300676
74	0.297009892546491	0.594019785092983	0.702990107453509
75	0.258016489662158	0.516032979324315	0.741983510337842
76	0.232643255031603	0.465286510063206	0.767356744968397
77	0.202852968896818	0.405705937793637	0.797147031103182
78	0.188166954699755	0.376333909399509	0.811833045300245
79	0.225788318671188	0.451576637342377	0.774211681328812
80	0.192520228057152	0.385040456114304	0.807479771942848
81	0.172830312136025	0.345660624272051	0.827169687863975
82	0.207144822238433	0.414289644476867	0.792855177761567
83	0.184408170995585	0.36881634199117	0.815591829004415
84	0.179228846261684	0.358457692523367	0.820771153738316
85	0.191864456159965	0.383728912319929	0.808135543840035
86	0.163850707524886	0.327701415049772	0.836149292475114
87	0.171778550083256	0.343557100166513	0.828221449916744
88	0.151138530407831	0.302277060815661	0.84886146959217
89	0.123681783149396	0.247363566298791	0.876318216850604
90	0.108358773241003	0.216717546482006	0.891641226758997
91	0.0910685407041655	0.182137081408331	0.908931459295835
92	0.146593380615687	0.293186761231374	0.853406619384313
93	0.122612825843683	0.245225651687366	0.877387174156317
94	0.112855561800831	0.225711123601663	0.887144438199169
95	0.130765430482317	0.261530860964635	0.869234569517683
96	0.110396545632809	0.220793091265619	0.88960345436719
97	0.128264499391441	0.256528998782882	0.87173550060856
98	0.102761665338059	0.205523330676119	0.89723833466194
99	0.081758794480539	0.163517588961078	0.918241205519461
100	0.0755080641164367	0.151016128232873	0.924491935883563
101	0.0859245646642239	0.171849129328448	0.914075435335776
102	0.0681278767045739	0.136255753409148	0.931872123295426
103	0.117135559566267	0.234271119132533	0.882864440433733
104	0.17049671393346	0.34099342786692	0.82950328606654
105	0.243855816953984	0.487711633907968	0.756144183046016
106	0.206549351775906	0.413098703551813	0.793450648224094
107	0.183222615491473	0.366445230982947	0.816777384508527
108	0.157561894206007	0.315123788412014	0.842438105793993
109	0.130512782879765	0.261025565759531	0.869487217120235
110	0.103404054549713	0.206808109099427	0.896595945450287
111	0.114065917176547	0.228131834353094	0.885934082823453
112	0.095175348761962	0.190350697523924	0.904824651238038
113	0.0756478485764306	0.151295697152861	0.92435215142357
114	0.0615319452603915	0.123063890520783	0.938468054739608
115	0.0783537439474994	0.156707487894999	0.9216462560525
116	0.0745178684030468	0.149035736806094	0.925482131596953
117	0.0793992626669348	0.15879852533387	0.920600737333065
118	0.143401801454101	0.286803602908202	0.856598198545899
119	0.157182214457162	0.314364428914324	0.842817785542838
120	0.146674126491606	0.293348252983211	0.853325873508394
121	0.114949359639427	0.229898719278854	0.885050640360573
122	0.111848278577301	0.223696557154602	0.888151721422699
123	0.0835713559299472	0.167142711859894	0.916428644070053
124	0.059976277029875	0.11995255405975	0.940023722970125
125	0.100317181521463	0.200634363042925	0.899682818478537
126	0.339917985877327	0.679835971754654	0.660082014122673
127	0.264336149961123	0.528672299922246	0.735663850038877
128	0.263035685593574	0.526071371187148	0.736964314406426
129	0.197215822933237	0.394431645866473	0.802784177066763
130	0.141066261385966	0.282132522771933	0.858933738614034
131	0.0927751678938345	0.185550335787669	0.907224832106165
132	0.180004854099909	0.360009708199817	0.819995145900091
133	0.152508780529239	0.305017561058478	0.847491219470761
134	0.0893802370276547	0.178760474055309	0.910619762972345
135	0.0442177717652123	0.0884355435304245	0.955782228234788

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	1	0.008	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292270254hirx8j083vo4k4i/10r1jg1292269393.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292270254hirx8j083vo4k4i/10r1jg1292269393.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292270254hirx8j083vo4k4i/1li441292269393.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292270254hirx8j083vo4k4i/1li441292269393.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292270254hirx8j083vo4k4i/2li441292269393.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292270254hirx8j083vo4k4i/2li441292269393.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292270254hirx8j083vo4k4i/3d9lp1292269393.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292270254hirx8j083vo4k4i/3d9lp1292269393.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292270254hirx8j083vo4k4i/4d9lp1292269393.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292270254hirx8j083vo4k4i/4d9lp1292269393.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292270254hirx8j083vo4k4i/5d9lp1292269393.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292270254hirx8j083vo4k4i/5d9lp1292269393.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292270254hirx8j083vo4k4i/6ojks1292269393.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292270254hirx8j083vo4k4i/6ojks1292269393.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292270254hirx8j083vo4k4i/7hajd1292269393.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292270254hirx8j083vo4k4i/7hajd1292269393.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292270254hirx8j083vo4k4i/8hajd1292269393.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292270254hirx8j083vo4k4i/8hajd1292269393.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292270254hirx8j083vo4k4i/9hajd1292269393.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292270254hirx8j083vo4k4i/9hajd1292269393.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice)

library(lmtest)

n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test

par1 <- as.numeric(par1)

x <- t(y)

k <- length(x[1,])

n <- length(x[,1])

x1 <- cbind(x[,par1], x[,1:k!=par1])

mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])

colnames(x1) <- mycolnames #colnames(x)[par1]

x <- x1

if (par3 == 'First Differences'){

x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))

for (i in 1:n-1) {

for (j in 1:k) {

x2[i,j] <- x[i+1,j] - x[i,j]

}

}

x <- x2

}

if (par2 == 'Include Monthly Dummies'){

x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))

for (i in 1:11){

x2[seq(i,n,12),i] <- 1

}

x <- cbind(x, x2)

}

if (par2 == 'Include Quarterly Dummies'){

x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))

for (i in 1:3){

x2[seq(i,n,4),i] <- 1

}

x <- cbind(x, x2)

}

k <- length(x[1,])

if (par3 == 'Linear Trend'){

x <- cbind(x, c(1:n))

colnames(x)[k+1] <- 't'

}

x

k <- length(x[1,])

df <- as.data.frame(x)

(mylm <- lm(df))

(mysum <- summary(mylm))

if (n > n25) {

kp3 <- k + 3

nmkm3 <- n - k - 3

gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))

numgqtests <- 0

numsignificant1 <- 0

numsignificant5 <- 0

numsignificant10 <- 0

for (mypoint in kp3:nmkm3) {

j <- 0

numgqtests <- numgqtests + 1

for (myalt in c('greater', 'two.sided', 'less')) {

j <- j + 1

gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value

}

if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1

if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1

if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1

}

gqarr

}

bitmap(file='test0.png')

plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')

points(x[,1]-mysum$resid)

grid()

dev.off()

bitmap(file='test1.png')

plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')

grid()

dev.off()

bitmap(file='test2.png')

hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')

grid()

dev.off()

bitmap(file='test3.png')

densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')

dev.off()

bitmap(file='test4.png')

qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')

qqline(mysum$resid)

grid()

dev.off()

(myerror <- as.ts(mysum$resid))

bitmap(file='test5.png')

dum <- cbind(lag(myerror,k=1),myerror)

dum

dum1 <- dum[2:length(myerror),]

dum1

z <- as.data.frame(dum1)

z

plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')

lines(lowess(z))

abline(lm(z))

grid()

dev.off()

bitmap(file='test6.png')

acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')

grid()

dev.off()

bitmap(file='test7.png')

pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')

grid()

dev.off()

bitmap(file='test8.png')

opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))

plot(mylm, las = 1, sub='Residual Diagnostics')

par(opar)

dev.off()

if (n > n25) {

bitmap(file='test9.png')

plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')

grid()

dev.off()

}

load(file='createtable')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)

a<-table.row.end(a)

myeq <- colnames(x)[1]

myeq <- paste(myeq, '[t] = ', sep='')

for (i in 1:k){

if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')

myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')

if (rownames(mysum$coefficients)[i] != '(Intercept)') {

myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')

if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')

}

}

myeq <- paste(myeq, ' + e[t]')

a<-table.row.start(a)

a<-table.element(a, myeq)

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable1.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Variable',header=TRUE)

a<-table.element(a,'Parameter',header=TRUE)

a<-table.element(a,'S.D.',header=TRUE)

a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE)

a<-table.element(a,'2-tail p-value',header=TRUE)

a<-table.element(a,'1-tail p-value',header=TRUE)

a<-table.row.end(a)

for (i in 1:k){

a<-table.row.start(a)

a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)

a<-table.element(a,mysum$coefficients[i,1])

a<-table.element(a, round(mysum$coefficients[i,2],6))

a<-table.element(a, round(mysum$coefficients[i,3],4))

a<-table.element(a, round(mysum$coefficients[i,4],6))

a<-table.element(a, round(mysum$coefficients[i,4]/2,6))

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable2.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Multiple R',1,TRUE)

a<-table.element(a, sqrt(mysum$r.squared))

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'R-squared',1,TRUE)

a<-table.element(a, mysum$r.squared)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Adjusted R-squared',1,TRUE)

a<-table.element(a, mysum$adj.r.squared)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (value)',1,TRUE)

a<-table.element(a, mysum$fstatistic[1])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)

a<-table.element(a, mysum$fstatistic[2])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)

a<-table.element(a, mysum$fstatistic[3])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'p-value',1,TRUE)

a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Residual Standard Deviation',1,TRUE)

a<-table.element(a, mysum$sigma)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Sum Squared Residuals',1,TRUE)

a<-table.element(a, sum(myerror*myerror))

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable3.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Time or Index', 1, TRUE)

a<-table.element(a, 'Actuals', 1, TRUE)

a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE)

a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE)

a<-table.row.end(a)

for (i in 1:n) {

a<-table.row.start(a)

a<-table.element(a,i, 1, TRUE)

a<-table.element(a,x[i])

a<-table.element(a,x[i]-mysum$resid[i])

a<-table.element(a,mysum$resid[i])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable4.tab')

if (n > n25) {

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'p-values',header=TRUE)

a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'breakpoint index',header=TRUE)

a<-table.element(a,'greater',header=TRUE)

a<-table.element(a,'2-sided',header=TRUE)

a<-table.element(a,'less',header=TRUE)

a<-table.row.end(a)

for (mypoint in kp3:nmkm3) {

a<-table.row.start(a)

a<-table.element(a,mypoint,header=TRUE)

a<-table.element(a,gqarr[mypoint-kp3+1,1])

a<-table.element(a,gqarr[mypoint-kp3+1,2])

a<-table.element(a,gqarr[mypoint-kp3+1,3])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable5.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Description',header=TRUE)

a<-table.element(a,'# significant tests',header=TRUE)

a<-table.element(a,'% significant tests',header=TRUE)

a<-table.element(a,'OK/NOK',header=TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'1% type I error level',header=TRUE)

a<-table.element(a,numsignificant1)

a<-table.element(a,numsignificant1/numgqtests)

if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'5% type I error level',header=TRUE)

a<-table.element(a,numsignificant5)

a<-table.element(a,numsignificant5/numgqtests)

if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'10% type I error level',header=TRUE)

a<-table.element(a,numsignificant10)

a<-table.element(a,numsignificant10/numgqtests)

if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable6.tab')

}

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

We may request personal information to be submitted to our servers in order to be able to:

personalize online software applications according to your needs
enforce strict security rules with respect to the data that you upload (e.g. statistical data)
manage user sessions of online applications
alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by